PS-TTS: Phonetic Synchronization in Text-to-Speech for Achieving Natural Automated Dubbing

View PDF Abstract:Recently, artificial intelligence-based dubbing technology has advanced, enabling automated dubbing (AD) to convert the source speech of a video into target speech in different languages. However, natural AD still faces synchronization challenges such as duration and lip-synchronization (lip-sync), which are crucial for preserving the viewer experience. Therefore, this paper proposes a synchronization method for AD processes that paraphrases translated text, comprising two steps: isochrony for timing constraints and phonetic synchronization (PS) to preserve lip-sync. First, we achieve isochrony by paraphrasing the translated text with a language model, ensuring the target speech duration matches that of the source speech. Second, we introduce PS, which employs dynamic time warping (DTW) with local costs of vowel distances measured from training data so that the target text composes vowels with pronunciations similar to source vowels. Third, we extend this approach to PSComet, which jointly considers semantic and phonetic similarity to preserve meaning better. The proposed methods are incorporated into text-to-speech systems, PS-TTS and PS-Comet TTS. The performance evaluation using Korean and English lip-reading datasets and a voice-actor dubbing dataset demonstrates that both systems outperform TTS without PS on several objective metrics and outperform voice actors in Korean-to-English and English-to-Korean dubbing. We extend the experiments to French, testing all pairs among these languages to evaluate cross-linguistic applicability. Across all language pairs, PS-Comet performed best, balancing lip-sync accuracy with semantic preservation, confirming that PS-Comet achieves more accurate lip-sync with semantic preservation than PS alone. Comments: Accepted to ICPR 2026 Subjects: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI) Cite as: arXiv:2604.09111 [eess.AS] (or arXiv:2604.09111v3 [eess.AS] for this version) https://doi.org/10.48550/arXiv.2604.09111 arXiv-issued DOI via DataCite Submission history From: Yoonah Song [view email] [v1] Fri, 10 Apr 2026 08:42:45 UTC (879 KB) [v2] Mon, 13 Apr 2026 06:46:03 UTC (877 KB) [v3] Tue, 14 Apr 2026 01:51:32 UTC (877 KB)